hdygxsj opened a new issue, #9385: URL: https://github.com/apache/gravitino/issues/9385
## Context As data ecosystems grow increasingly complex—spanning multiple engines (Trino, Spark, Flink), table formats (Paimon, Iceberg, Delta) — I believe metadata management must evolve beyond passive cataloging. Gravitino has a unique opportunity to become an AI-native metadata governance platform that proactively helps users design, discover, secure, and optimize their data assets. I’d like to propose integrating LangChain4j (the Java-native implementation of LangChain) to unlock intelligent, LLM-powered capabilities directly within Gravitino’s metadata layer. ## Capabilities I’d Like to See ### Post-Creation AI Assessment of Table Design After a table is created (e.g., via DDL), I propose triggering an asynchronous AI evaluation to assess: Partitioning strategy Indexing opportunities Format and storage options—especially Paimon-specific configurations like bucket, changelog-producer, and merge-engine The system could then provide actionable, natural-language recommendations to improve performance, cost, and correctness. ### Semantic Auto-Tagging of Tables and Columns I suggest using LLMs and embedding models to automatically infer and apply standardized tags based on: Column/table names (user_id, ssn, risk_score) Business context (via RAG over internal glossaries or compliance policies) Examples: fee-amount, price-amount, cost-amount ### RAG-Powered Detection of Similar Tables To reduce redundancy, I’d like Gravitino to detect semantically similar existing tables across catalogs when a new table is being created. By building a vector index of table embeddings (schema + description + usage patterns), the system could, on CREATE TABLE, retrieve similar tables and generate a comparison report via LLM: “A similar table web_events already exists (92% similarity). Consider reusing or merging.” ### Natural Language Table Understanding (NL2Insight) I envision users asking questions like: “Which tables contain monetary or amount-related fields?” “Where is customer order information stored?” “Show me tables with user behavior logs from mobile apps.” “Do we have any table tracking refund events?” Welcome to discuss together! -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
